| Computational multi-clusters are an important emerging class of supercomputing architectures. As multi-cluster systems become more prevalent, techniques for efficiently exploiting these resources become increasingly significant. A critical aspect of exploiting these resources is the challenge of scheduling. In particular, multi-cluster schedulers must address not only node resource allocation but also inter-cluster network utilization. Intelligent schedulers can make use of specific architectural features such as topology and link utilization as well as parallel program attributes such as job communication structure to improve average job response time.; This research results in several contributions. Firstly, it identifies several of the parameters necessary to make intelligent scheduling decisions with respect to job co-allocation in a multi-cluster. It demonstrates that by making use of these parameters, a job scheduler can significantly improve job throughput by carefully managing node resources as well as shared inter-cluster network bandwidth. This work establishes the relationship between the amount of specific job and network attributes that are available to the scheduler and the performance that can be achieved by making use of that information. This research also demonstrates the dramatic impact that salient workload characteristics can have on the effectiveness of parallel job scheduling in multi-clusters. Finally, it demonstrates that these optimizations can be successfully integrated with policies that ensure fairness among participating clusters. |
